In signal processing, a signal that is to be measured and the measurement process itself can contain noise, as well as contribute to an amount of noise. Noise effects signal processing, so it is better to eliminate noise to obtain a better signal processing result, such as sharper images. Typically, the process of signal measurement requires a significant amount of processing time, which can be a challenge for today's technological applications that require less processing time and a given quality of a result.
For example, if a measured signal is to be transferred to some other location, some advantageous to sending the measured signal may include reducing the amount of data being sent to as small as possible so as to lower the required bandwidth. Another advantage to sending the measured signal may include increasing the rate of sending the measured signal (complete measurements), such as the frame rate for video signal transmissions.
Sparse representation of signals is a signal processing technology in which noise, which cannot be represented sparsely, can be filtered out. The sparse representation of a given signal can be estimated from a small number of measurements, where small is compared to the dimension of the signal. Also, a sparse representation generally means that the data is compressed.
There are numerous sparse representation learning algorithms known in the art which are used for a broad range of signal and/or image processing applications. For instance, sparse representations may be used for labeling objects in images to identify and classify what objects are present. Given a signal s and a dictionary matrix D, sparse coding is the inverse problem of finding the sparse representation x with only a few non-zero entries such that Dx≈s. The process of arriving at these identifications requires a technique for learning the dictionary matrix D, referred herein as dictionary learning. Further, learning the dictionary matrix using the sparse representation may be beneficial for some applications. However, offline learning limits the dictionary matrix to represent only images used for the training. Thus, the offline learned dictionary lacks the flexibility and adaptability to the variety of images that can be accounted in real time.
Also, these sparse representation learning algorithms are not scalable to large data dimensional inputs, and are conventionally viewed as not suited for some technological applications.
There are known hardware designs for operating on large data sets, e.g. large matrix multiplications and neural network simulations. Neural network simulators are known that typically use mixed analog-digital signals, but these make it harder to scale up the hardware. Also, in digital signal operations the bandwidth with which data can be downloaded to the hardware limits the practical size of the hardware.
Accordingly, there is a need for systems and methods suitable for acquiring measurements of an object using multiple distinct sensing modalities. Wherein data acquired from the sensors can be jointly processed to improve the imaging quality in one or more of the acquired modalities. Which can result in providing complementary sources of information about objects, overcoming hardware limitations, or reducing data uncertainty due to each individual sensor.